{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "L4",
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LEj76j5G4INX",
        "outputId": "e3d86aee-1bd7-4ddd-c485-de46d0001114"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !git clone https://github.com/THUDM/GLM-4.git"
      ],
      "metadata": {
        "id": "4yho52TV5-eG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nLGQV7b-Khe",
        "outputId": "da59543f-f66c-4218-d29b-01f506188455"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drive  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_-dBXnb-Ouc",
        "outputId": "c6b1d795-eb35-4a5b-eba3-7f3e0ec64217"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " ChatGLM3  'Colab Notebooks'   GLM-4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 这里的微调数据集格式与glm3的不一样，请使用给大家的数据集\n",
        "# %cd GLM-4\n",
        "# !cp -r /content/drive/MyDrive/AdvertiseGen_fix/ finetune_demo/\n",
        "# %cd ..\n",
        "# !mv GLM-4/ /content/drive/MyDrive/"
      ],
      "metadata": {
        "id": "vLsbdlwj9ljc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/GLM-4/finetune_demo/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hssdYc0n-5kW",
        "outputId": "229e12b1-d12a-4549-d2c8-2d978f4cc574"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/GLM-4/finetune_demo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 这一条需要修改peft==0.12.0\n",
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_u62q7Xc8eun",
        "outputId": "2fbb0270-62b0-4606-94f1-25ddd6f30835"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: jieba==0.42.1 in /usr/local/lib/python3.11/dist-packages (from -r requirements.txt (line 1)) (0.42.1)\n",
            "Collecting datasets==2.20.0 (from -r requirements.txt (line 2))\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Downloading nltk-3.8.1-py3-none-any.whl.metadata (2.8 kB)\n",
            "Collecting rouge_chinese==1.0.3 (from -r requirements.txt (line 6))\n",
            "  Downloading rouge_chinese-1.0.3-py3-none-any.whl.metadata (7.6 kB)\n",
            "Collecting ruamel.yaml==0.18.6 (from -r requirements.txt (line 7))\n",
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            "  Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess (from datasets==2.20.0->-r requirements.txt (line 2))\n",
            "  Downloading multiprocess-0.70.17-py311-none-any.whl.metadata (7.2 kB)\n",
            "Collecting fsspec<=2024.5.0,>=2023.1.0 (from fsspec[http]<=2024.5.0,>=2023.1.0->datasets==2.20.0->-r requirements.txt (line 2))\n",
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            "  Downloading hjson-3.1.0-py3-none-any.whl.metadata (2.6 kB)\n",
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            "\u001b[?25hDownloading ruamel.yaml.clib-0.2.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (739 kB)\n",
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            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
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            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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            "\u001b[?25hDownloading hjson-3.1.0-py3-none-any.whl (54 kB)\n",
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            "\u001b[?25hDownloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n",
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            "\u001b[?25hDownloading ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (422 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m422.8/422.8 kB\u001b[0m \u001b[31m32.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
            "Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: deepspeed\n",
            "  Building wheel for deepspeed (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for deepspeed: filename=deepspeed-0.14.4-py3-none-any.whl size=1445509 sha256=b8711887b218e4179170e45d55ff5769c4d3ad133196eccdc151f722dbc10cf2\n",
            "  Stored in directory: /root/.cache/pip/wheels/8e/bb/99/b092ccb948b1383222922cd793a36eb783ef99b66c57d75758\n",
            "Successfully built deepspeed\n",
            "Installing collected packages: hjson, xxhash, ruamel.yaml.clib, rouge_chinese, pyarrow-hotfix, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nltk, ninja, fsspec, dill, ruamel.yaml, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, datasets, deepspeed, peft\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nltk\n",
            "    Found existing installation: nltk 3.9.1\n",
            "    Uninstalling nltk-3.9.1:\n",
            "      Successfully uninstalled nltk-3.9.1\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2025.3.0\n",
            "    Uninstalling fsspec-2025.3.0:\n",
            "      Successfully uninstalled fsspec-2025.3.0\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "  Attempting uninstall: peft\n",
            "    Found existing installation: peft 0.14.0\n",
            "    Uninstalling peft-0.14.0:\n",
            "      Successfully uninstalled peft-0.14.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2024.5.0 which is incompatible.\n",
            "textblob 0.19.0 requires nltk>=3.9, but you have nltk 3.8.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 deepspeed-0.14.4 dill-0.3.8 fsspec-2024.5.0 hjson-3.1.0 multiprocess-0.70.16 ninja-1.11.1.4 nltk-3.8.1 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 peft-0.12.0 pyarrow-hotfix-0.6 rouge_chinese-1.0.3 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.12 xxhash-3.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip list|grep dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLg48G9X7LIj",
        "outputId": "47eb311b-3d34-42f2-ad82-573f257515d1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "datasets                           2.20.0\n",
            "tensorflow-datasets                4.9.8\n",
            "vega-datasets                      0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tiktoken"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1Iyg8tECJrd",
        "outputId": "23ad3bf0-ecaf-4028-aeba-44ab983c748e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tiktoken\n",
            "  Downloading tiktoken-0.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
            "Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.11/dist-packages (from tiktoken) (2024.11.6)\n",
            "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.11/dist-packages (from tiktoken) (2.32.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.26.0->tiktoken) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.26.0->tiktoken) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.26.0->tiktoken) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.26.0->tiktoken) (2025.1.31)\n",
            "Downloading tiktoken-0.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tiktoken\n",
            "Successfully installed tiktoken-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install transformers==4.48.1"
      ],
      "metadata": {
        "id": "JBsG3ZTEvP6p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python finetune.py  ./AdvertiseGen_fix/  THUDM/glm-4-9b-chat  configs/lora.yaml\n",
        "# !python finetune.py  ./AdvertiseGen_fix/  THUDM/glm-4-9b-chat  configs/lora.yaml # For Chat Fine-tune\n",
        "# python finetune_vision.py  data/CogVLM-311K/  THUDM/glm-4v-9b configs/lora.yaml # For VQA Fine-tune"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJco__BK8hMc",
        "outputId": "2b207c9d-5316-47ff-e4f7-00142792ab49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2025-02-08 09:43:09.669182: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2025-02-08 09:43:09.688009: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
            "E0000 00:00:1739007789.710454    9864 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "E0000 00:00:1739007789.717169    9864 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2025-02-08 09:43:09.740027: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "Loading checkpoint shards: 100% 10/10 [00:03<00:00,  3.10it/s]\n",
            "trainable params: 2,785,280 || all params: 9,402,736,640 || trainable%: 0.0296\n",
            "train_dataset: Dataset({\n",
            "    features: ['input_ids', 'labels'],\n",
            "    num_rows: 114599\n",
            "})\n",
            "val_dataset: Dataset({\n",
            "    features: ['input_ids', 'output_ids'],\n",
            "    num_rows: 1070\n",
            "})\n",
            "test_dataset: Dataset({\n",
            "    features: ['input_ids', 'output_ids'],\n",
            "    num_rows: 1070\n",
            "})\n",
            "[2025-02-08 09:43:27,912] [INFO] [real_accelerator.py:222:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
            "max_steps is given, it will override any value given in num_train_epochs\n",
            "/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 12, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 114,599\n",
            "  Num Epochs = 1\n",
            "  Instantaneous batch size per device = 1\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 1\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 3,000\n",
            "  Number of trainable parameters = 2,785,280\n",
            "Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mlizhilong1987\u001b[0m to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.19.6\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/drive/MyDrive/GLM-4/finetune_demo/wandb/run-20250208_094330-ksxxkxv2\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33m./output\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/lizhilong1987/huggingface\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/lizhilong1987/huggingface/runs/ksxxkxv2\u001b[0m\n",
            "  0% 0/3000 [00:00<?, ?it/s]/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 12, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/MyDrive/GLM-4/finetune_demo/finetune.py\", line 508, in <module>\n",
            "    app()\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/typer/main.py\", line 340, in __call__\n",
            "    raise e\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/typer/main.py\", line 323, in __call__\n",
            "    return get_command(self)(*args, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/click/core.py\", line 1161, in __call__\n",
            "    return self.main(*args, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/typer/core.py\", line 680, in main\n",
            "    return _main(\n",
            "           ^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/typer/core.py\", line 198, in _main\n",
            "    rv = self.invoke(ctx)\n",
            "         ^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/click/core.py\", line 1443, in invoke\n",
            "    return ctx.invoke(self.callback, **ctx.params)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/click/core.py\", line 788, in invoke\n",
            "    return __callback(*args, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/typer/main.py\", line 698, in wrapper\n",
            "    return callback(**use_params)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/content/drive/MyDrive/GLM-4/finetune_demo/finetune.py\", line 471, in main\n",
            "    trainer.train()\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\", line 2171, in train\n",
            "    return inner_training_loop(\n",
            "           ^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/transformers/trainer.py\", line 2531, in _inner_training_loop\n",
            "    tr_loss_step = self.training_step(model, inputs, num_items_in_batch)\n",
            "                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "TypeError: Seq2SeqTrainer.training_step() takes 3 positional arguments but 4 were given\n",
            "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
            "\u001b[31m│\u001b[0m \u001b[2;33m/content/drive/MyDrive/GLM-4/finetune_demo/\u001b[0m\u001b[1;33mfinetune.py\u001b[0m:\u001b[94m471\u001b[0m in \u001b[92mmain\u001b[0m                               \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m468 \u001b[0m\u001b[2m│   \u001b[0m)                                                                                      \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m469 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m470 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mif\u001b[0m auto_resume_from_checkpoint.upper() == \u001b[33m\"\u001b[0m\u001b[33m\"\u001b[0m \u001b[95mor\u001b[0m auto_resume_from_checkpoint \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m:   \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m471 \u001b[2m│   │   \u001b[0m\u001b[1;4mtrainer.train()\u001b[0m                                                                    \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m472 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94melse\u001b[0m:                                                                                  \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m473 \u001b[0m\u001b[2m│   │   \u001b[0moutput_dir = ft_config.training_args.output_dir                                    \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m474 \u001b[0m\u001b[2m│   │   \u001b[0mdirlist = os.listdir(output_dir)                                                   \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.11/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2171\u001b[0m in \u001b[92mtrain\u001b[0m                    \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2168 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mfinally\u001b[0m:                                                                      \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2169 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mhf_hub_utils.enable_progress_bars()                                       \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2170 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                             \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2171 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m inner_training_loop(                                                   \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2172 \u001b[0m\u001b[2m│   │   │   │   \u001b[0margs=args,                                                                \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2173 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mresume_from_checkpoint=resume_from_checkpoint,                            \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2174 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mtrial=trial,                                                              \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.11/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2531\u001b[0m in \u001b[92m_inner_training_loop\u001b[0m     \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2528 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m\u001b[94melse\u001b[0m contextlib.nullcontext                                       \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2529 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m)                                                                     \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2530 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[94mwith\u001b[0m context():                                                       \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2531 \u001b[2m│   │   │   │   │   │   \u001b[0mtr_loss_step = \u001b[1;4;96mself\u001b[0m\u001b[1;4m.training_step(model, inputs, num_items_in_ba\u001b[0m  \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2532 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m                                                                      \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2533 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0m\u001b[94mif\u001b[0m (                                                                  \u001b[31m│\u001b[0m\n",
            "\u001b[31m│\u001b[0m   \u001b[2m2534 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0margs.logging_nan_inf_filter                                       \u001b[31m│\u001b[0m\n",
            "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
            "\u001b[1;91mTypeError: \u001b[0m\u001b[1;35mSeq2SeqTrainer.training_step\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m takes \u001b[1;36m3\u001b[0m positional arguments but \u001b[1;36m4\u001b[0m were given\n",
            "\u001b[1;34mwandb\u001b[0m: \n",
            "\u001b[1;34mwandb\u001b[0m: 🚀 View run \u001b[33m./output\u001b[0m at: \u001b[34mhttps://wandb.ai/lizhilong1987/huggingface/runs/ksxxkxv2\u001b[0m\n",
            "\u001b[1;34mwandb\u001b[0m: Find logs at: \u001b[1;35mwandb/run-20250208_094330-ksxxkxv2/logs\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.listdir('output/checkpoint-3000')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DxYHCxfaYw7R",
        "outputId": "b8605bd2-dbf4-464a-cc3f-c5f6f47b88c9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['optimizer.pt',\n",
              " 'rng_state.pth',\n",
              " 'scheduler.pt',\n",
              " 'adapter_model.safetensors',\n",
              " 'adapter_config.json',\n",
              " 'README.md',\n",
              " 'trainer_state.json',\n",
              " 'training_args.bin']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#默认使用的原始大模型路径就是在系统下，这里跟的是微调后的权重\n",
        "#如果要预测其他内容，直接修改inference.py中的messages即可，也可以修改代码，传参argv\n",
        "!python inference.py output/checkpoint-3000"
      ],
      "metadata": {
        "id": "nSiWor7u9LaX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b26f44dc-f4e6-4085-c6d9-8f4b8693b662"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2025-03-24 03:57:49.073215: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2025-03-24 03:57:49.090757: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
            "E0000 00:00:1742788669.113001    6360 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "E0000 00:00:1742788669.119937    6360 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2025-03-24 03:57:49.142670: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "config.json: 100% 1.44k/1.44k [00:00<00:00, 12.3MB/s]\n",
            "configuration_chatglm.py: 100% 2.27k/2.27k [00:00<00:00, 18.5MB/s]\n",
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/glm-4-9b-chat:\n",
            "- configuration_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "modeling_chatglm.py: 100% 47.9k/47.9k [00:00<00:00, 4.59MB/s]\n",
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/glm-4-9b-chat:\n",
            "- modeling_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "model.safetensors.index.json: 100% 29.1k/29.1k [00:00<00:00, 24.8MB/s]\n",
            "Downloading shards:   0% 0/10 [00:00<?, ?it/s]\n",
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            "A new version of the following files was downloaded from https://huggingface.co/THUDM/glm-4-9b-chat:\n",
            "- tokenization_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
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            "=========\n",
            "<UNK>裙子的出现，真的是让人眼前一亮。它真的可以给我们的日常生活带来无限的惊喜和乐趣哦！当然啦，它的颜色也超级的多变，无论是深色还是浅色的款式都非常的有个性呢！这个夏季你一定不要错过哦！\n"
          ]
        }
      ]
    }
  ]
}